Tootfinder

Utilizing dynamic sparsity on pretrained DETR
Reza Sedghi, Anand Subramoney, David Kappel
https://arxiv.org/abs/2510.09380 https://arxiv.org/pdf/2510.09380…

Utilizing dynamic sparsity on pretrained DETR
Efficient inference with transformer-based models remains a challenge, especially in vision tasks like object detection. We analyze the inherent sparsity in the MLP layers of DETR and introduce two methods to exploit it without retraining. First, we propose Static Indicator-Based Sparsification (SIBS), a heuristic method that predicts neuron inactivity based on fixed activation patterns. While simple, SIBS offers limited gains due to the input-dependent nature of sparsity. To address this, we i…

Comparing Knowledge Source Integration Methods for Optimizing Healthcare Knowledge Fusion in Rescue Operation
Mubaris Nadeem, Madjid Fathi
https://arxiv.org/abs/2510.09223 https…

Comparing Knowledge Source Integration Methods for Optimizing Healthcare Knowledge Fusion in Rescue Operation
In the field of medicine and healthcare, the utilization of medical expertise, based on medical knowledge combined with patients' health information is a life-critical challenge for patients and health professionals. The within-laying complexity and variety form the need for a united approach to gather, analyze, and utilize existing knowledge of medical treatments, and medical operations to provide the ability to present knowledge for the means of accurate patient-driven decision-making. One wa…

«C’est peut-être un peu surprenant sur le plan de la gestion.» Eh ben, oui.
https://www.rts.ch/info/suisse/2025/article/la-ssr-revient-sur-la-fm-apres-la-decision-du-parlement-de-maintenir-la-bande-2908795…

É, aqui hoje estš um calorzinho.

La vigilance est plus que jamais nécessaire : des organisations piègent les robots conversationnels pour les amener Š faire installer un logiciel malveillant…
https://www.macg.co/intelligence-artificie

Large Language Model Prompt Datasets: An In-depth Analysis and Insights
Yuanming Zhang, Yan Lin, Arijit Khan, Huaiyu Wan
https://arxiv.org/abs/2510.09316 https://

Large Language Model Prompt Datasets: An In-depth Analysis and Insights
A prompt is a natural language instruction that defines a specific task for a large language model (LLM) and serves as the primary interface for human-LLM interaction. With the growing deployment of LLMs, diverse prompt datasets are emerging from platforms such as GitHub and social media. These datasets span a wide array of applications and content types, facilitating both broader LLM utilization and improved prompt engineering. In this work, we--for the first time--have compiled an extensive l…

hey wanna see some completely normal systemverilog? https://github.com/lowRISC/opentitan/blob/e383c23ab5d44940a07652ccd31406166286073d/hw/dv/sv/dv_utils/dv_utils_pkg.sv#L210-L355

opentitan/hw/dv/sv/dv_utils/dv_utils_pkg.sv at e383c23ab5d44940a07652ccd31406166286073d · lowRISC/opentitan
OpenTitan: Open source silicon root of trust. Contribute to lowRISC/opentitan development by creating an account on GitHub.

Logit Arithmetic Elicits Long Reasoning Capabilities Without Training
Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang, Xin Liu, Ayoung Lee, Xinliang Frederick Zhang, Farima Fatahi Bayat, Lu Wang
https://arxiv.org/abs/2510.09354

Logit Arithmetic Elicits Long Reasoning Capabilities Without Training
Large reasoning models exhibit long chain-of-thought reasoning with strategies such as backtracking and self-correction, though recent studies suggest that these abilities typically require additional training. We first investigate whether such behaviors can be elicited without any training. To this end, we propose a decoding-time approach, ThinkLogit, which utilizes logit arithmetic to tune a target large non-reasoning model for long reasoning using a substantially smaller reasoning model as t…